ABSTRACT
Estimating economic indicators is crucial to achieving targeted implementation of welfare policies. However, for such policies to be effective policy makers must have access to a detailed picture of deprivation that goes beyond aggregate estimates at the country (national) level, extending to finer geographical levels and to other domains of interest, such as specific groups of individuals. Such a picture can only be constructed by having access to timely and accurate survey and administrative/Census data at appropriate spatial scales. One possible solution for obtaining accurate indicators at finer spatial scales is the use of small area estimation methodologies. The term ‘small areas’ is typically used to describe domains (e.g. geographic areas) whose sample sizes are not large enough to allow sufficiently precise direct estimation, i.e. estimation that is based only on the sample data from the domain (Rao, 2003). Small area-specific sample sizes often also hamper the use of conventional design-based estimators. In such cases model-based estimation procedures can be considered to improve the precision of the direct estimates.
